1 STRATAA microbiome taxonomic analysis

1.1 Imports

# library(rbiom)
# library(kableExtra)
# library(ggplot2)
# library(ggpubr)
# library(ggforce)
# library(patchwork)
# library(vegan)
# library(dplyr)
# library(forcats)
# library(RColorBrewer)
# library(randomForest)
# library(caret)

1.2 Sources

The file handles are set in config.R as they’re used by both this script and data_cleaning.

source("/Users/flashton/Dropbox/GordonGroup/STRATAA_Microbiome/from_Leo/Leonardos_analysis/bin/00.core_functions.R")
source("/Users/flashton/Dropbox/GordonGroup/STRATAA_Microbiome/from_Leo/Leonardos_analysis/bin/config.R")

1.3 Read in metadata

metadata <- read_metadata(metadata_handle)
# putting this here so that the output files of maaslin get named accroding to the variable names in the metadata file.
metadata <- metadata %>% mutate(Group = if_else(Group == 'Control_HealthySerosurvey', 'Household contact', Group)) %>% mutate(Group = if_else(Group == 'Acute_Typhi', 'Acute typhoid', Group)) %>% mutate(Group = if_else(Group == 'Carrier', 'High Vi-titre', Group))

1.4 read in metaphlan data

strataa_metaphlan_data <- read.csv(file = file.path(metaphlan_input_folder, '2023.05.11.all_strataa_metaphlan.csv'), header= TRUE, sep = ",", row.names = 1, stringsAsFactors = FALSE, check.names=FALSE)
strataa_metaphlan_data$lowest_taxonomic_level <- sapply(str_split(row.names(strataa_metaphlan_data), "\\|"), function(x) x[length(x)])
strataa_metaphlan_data_species <- strataa_metaphlan_data %>% filter(str_starts(lowest_taxonomic_level, 's__'))

# metadata <- read.csv(file = file.path(metaphlan_input_folder, '2023.05.11.strataa_metadata.metaphlan.csv'), header = TRUE, sep = ",", row.names = 1, stringsAsFactors = FALSE)

1.5 Healthy vs Typhi

1.5.1 alpha diversity

Alpha diversity - all countries, healthy and acute

all_countries_healthy_acute_alpha <- metaphlan_alpha(strataa_metaphlan_data_species, metadata, countries_of_interest = c('Bangladesh', 'Malawi', 'Nepal'), groups_of_interest = c('Acute typhoid', 'Household contact'), comparisons = list(c('Acute typhoid', 'Household contact')), participant_group_colours = participant_group_colours)

# all_countries_healthy_acute_alpha$alpha_by_country
all_countries_healthy_acute_alpha$alpha_anova_summary_with_var_names %>% dplyr::mutate_if(is.numeric, funs(as.character(signif(., 3)))) %>% kbl() %>% kable_styling()
rownames(alpha_anova_summary[[1]]) Df Sum.Sq Mean.Sq F.value Pr..F. is_it_significant
Country 2 16.3 8.17 18 9.26e-08 significant
Country:Age:Antibiotics_taken_before_sampling_assumptions 2 5.17 2.58 5.7 0.0041 significant
Country:Age 2 3.32 1.66 3.66 0.0279 not_significant
Sex:Age 1 1.99 1.99 4.4 0.0376 not_significant
Age:Antibiotics_taken_before_sampling_assumptions 1 1.51 1.51 3.32 0.0703 not_significant
Country:Sex 2 2.18 1.09 2.4 0.0941 not_significant
Sex:Group 1 1.12 1.12 2.46 0.119 not_significant
Country:Antibiotics_taken_before_sampling_assumptions 2 1.53 0.765 1.69 0.188 not_significant
Country:Group 2 1.24 0.622 1.37 0.257 not_significant
Country:Sex:Antibiotics_taken_before_sampling_assumptions 2 1.23 0.616 1.36 0.26 not_significant
Group 1 0.567 0.567 1.25 0.265 not_significant
Sex:Antibiotics_taken_before_sampling_assumptions 1 0.555 0.555 1.22 0.27 not_significant
Country:Sex:Group 2 0.759 0.379 0.837 0.435 not_significant
Age 1 0.227 0.227 0.502 0.48 not_significant
Country:Group:Age 2 0.627 0.313 0.692 0.502 not_significant
Antibiotics_taken_before_sampling_assumptions 1 0.195 0.195 0.43 0.513 not_significant
Country:Sex:Age 2 0.43 0.215 0.475 0.623 not_significant
Sex:Age:Antibiotics_taken_before_sampling_assumptions 1 0.0406 0.0406 0.0897 0.765 not_significant
Sex 1 0.0334 0.0334 0.0736 0.786 not_significant
Country:Sex:Group:Age 2 0.206 0.103 0.228 0.797 not_significant
Sex:Group:Age 1 0.027 0.027 0.0595 0.808 not_significant
Group:Age 1 0.0137 0.0137 0.0302 0.862 not_significant
Country:Sex:Age:Antibiotics_taken_before_sampling_assumptions 2 0.0969 0.0485 0.107 0.899 not_significant
Residuals 153 69.3 0.453 NA NA NA
all_countries_healthy_acute_alpha$alpha_plot_group

# all_countries_healthy_acute_alpha$alpha_plot_antibiotics

1.5.2 beta diversity

Acute vs healthy.

all_countries_beta_acute_healthy <- strataa_metaphlan_beta(strataa_metaphlan_data_species, metadata, c('Bangladesh', 'Malawi', 'Nepal'), c('Acute typhoid', 'Household contact'), participant_group_colours)

all_countries_beta_acute_healthy$pn_res %>% kbl %>% kable_styling()
R2 Pr(>F) is_it_significant
Group 0.0404140 0.0009990 significant
Group:Age 0.0096324 0.0109890 not_significant
Age 0.0084032 0.0479520 not_significant
Antibiotics_taken_before_sampling_assumptions 0.0074021 0.0779221 not_significant
Sex:Group:Age 0.0072938 0.0809191 not_significant
Sex:Age 0.0069815 0.1088911 not_significant
Sex:Age:Antibiotics_taken_before_sampling_assumptions 0.0062206 0.1558442 not_significant
Sex 0.0061693 0.1778222 not_significant
Sex:Antibiotics_taken_before_sampling_assumptions 0.0060463 0.2097902 not_significant
Sex:Group 0.0053365 0.3476523 not_significant
Age:Antibiotics_taken_before_sampling_assumptions 0.0049480 0.4355644 not_significant
Total 1.0000000 NA NA
Residual 0.8911522 NA NA
bgd_beta_acute_healthy <- strataa_metaphlan_beta(strataa_metaphlan_data_species, metadata, c('Bangladesh'), c('Acute typhoid', 'Household contact'), participant_group_colours)

bgd_beta_acute_healthy$pn_res %>% kbl %>% kable_styling()
R2 Pr(>F) is_it_significant
Group 0.0741793 0.0009990 significant
Age 0.0179478 0.0489510 not_significant
Sex 0.0169513 0.0539461 not_significant
Sex:Age 0.0144580 0.1828172 not_significant
Antibiotics_taken_before_sampling_assumptions 0.0140324 0.2317682 not_significant
Sex:Group:Age 0.0126416 0.3866134 not_significant
Group:Age 0.0120334 0.4005994 not_significant
Age:Antibiotics_taken_before_sampling_assumptions 0.0097011 0.7182817 not_significant
Sex:Antibiotics_taken_before_sampling_assumptions 0.0083380 0.8921079 not_significant
Sex:Group 0.0079615 0.9210789 not_significant
Sex:Age:Antibiotics_taken_before_sampling_assumptions 0.0061804 0.9760240 not_significant
Total 1.0000000 NA NA
Residual 0.8055752 NA NA
mal_beta_acute_healthy <- strataa_metaphlan_beta(strataa_metaphlan_data_species, metadata, c('Malawi'), c('Acute typhoid', 'Household contact'), participant_group_colours)

mal_beta_acute_healthy$pn_res %>% kbl %>% kable_styling()
R2 Pr(>F) is_it_significant
Group 0.2814220 0.0009990 significant
Age:Antibiotics_taken_before_sampling_assumptions 0.0264834 0.0039960 significant
Sex:Antibiotics_taken_before_sampling_assumptions 0.0233976 0.0289710 not_significant
Antibiotics_taken_before_sampling_assumptions 0.0216165 0.0379620 not_significant
Sex:Group 0.0214513 0.0399600 not_significant
Sex:Age:Antibiotics_taken_before_sampling_assumptions 0.0206438 0.0579421 not_significant
Sex:Age 0.0161503 0.1168831 not_significant
Sex 0.0149099 0.1378621 not_significant
Age 0.0154778 0.1428571 not_significant
Sex:Group:Age 0.0136252 0.2397602 not_significant
Group:Age 0.0107163 0.4055944 not_significant
Total 1.0000000 NA NA
Residual 0.5341059 NA NA
nep_beta_acute_healthy <- strataa_metaphlan_beta(strataa_metaphlan_data_species, metadata, c('Nepal'), c('Acute typhoid', 'Household contact'), participant_group_colours)

nep_beta_acute_healthy$pn_res %>% kbl %>% kable_styling()
R2 Pr(>F) is_it_significant
Group 0.0578851 0.0009990 significant
Sex 0.0424544 0.0029970 significant
Sex:Group 0.0269210 0.1238761 not_significant
Sex:Antibiotics_taken_before_sampling_assumptions 0.0256575 0.1578422 not_significant
Age 0.0231087 0.3046953 not_significant
Sex:Age:Antibiotics_taken_before_sampling_assumptions 0.0212328 0.4855145 not_significant
Sex:Age 0.0200241 0.4885115 not_significant
Sex:Group:Age 0.0201543 0.5344655 not_significant
Group:Age 0.0170470 0.7722278 not_significant
Antibiotics_taken_before_sampling_assumptions 0.0167425 0.7962038 not_significant
Age:Antibiotics_taken_before_sampling_assumptions 0.0152914 0.8781219 not_significant
Total 1.0000000 NA NA
Residual 0.7134813 NA NA
all_countries_beta_acute_healthy$pc12 + bgd_beta_acute_healthy$pc12 + mal_beta_acute_healthy$pc12 + nep_beta_acute_healthy$pc12 + plot_layout(guides = 'collect')

1.5.3 maaslin2 taxonomy

Maaslin basics

# the names here should be the full name from the metadata file, not the "presentation" name
# because this is used to read in the files, which are written using the full name.
groups_to_analyse <- c('Acute_Typhi', 'Control_HealthySerosurvey')
bang_variables_for_analysis <- c("Group", "Sex", "Age", "Antibiotics_taken_before_sampling_assumptions")
mwi_variables_for_analysis <- c("Group", "Sex", "Age", "Antibiotics_taken_before_sampling_assumptions", "sequencing_lane")
nep_variables_for_analysis <- c("Group", "Sex", "Age", "Antibiotics_taken_before_sampling_assumptions")
bangladesh_taxonomic_maaslin <- read_in_maaslin('Bangladesh', groups_to_analyse, bang_variables_for_analysis, 'metaphlan')
malawi_taxonomic_maaslin <- read_in_maaslin('Malawi', groups_to_analyse, mwi_variables_for_analysis, 'metaphlan')
nepal_taxonomic_maaslin <- read_in_maaslin('Nepal', groups_to_analyse, nep_variables_for_analysis, 'metaphlan')
bangladesh_taxonomic_maaslin_filtered <- filter_taxonomic_maaslin(bangladesh_taxonomic_maaslin)
malawi_taxonomic_maaslin_filtered <- filter_taxonomic_maaslin(malawi_taxonomic_maaslin)
nepal_taxonomic_maaslin_filtered <- filter_taxonomic_maaslin(nepal_taxonomic_maaslin)
bangladesh_maaslin_stats <- basic_maaslin_stats(bangladesh_taxonomic_maaslin_filtered, 'Bangladesh', bang_variables_for_analysis, groups_to_analyse)
malawi_maaslin_stats <- basic_maaslin_stats(malawi_taxonomic_maaslin_filtered, 'Malawi', mwi_variables_for_analysis, groups_to_analyse)
nepal_maaslin_stats <- basic_maaslin_stats(nepal_taxonomic_maaslin_filtered, 'Nepal', nep_variables_for_analysis, groups_to_analyse)

There were 92 species significantly (q-val < 0.05) associated with health/disease in Malawi, in Bangladesh, and in Nepal.

Combine the taxonomic maaslins, and print out the species that are sig in both and share directions.

Because sequencing run and participant type are totally confounded for Bangladesh, need to exclude sequencing run from the final model for Bangladesh (otherwise, wipes out the signals).

associated at both sites

bang_mal <- list(bangladesh_taxonomic_maaslin_filtered, malawi_taxonomic_maaslin_filtered)
combined_results <- run_inner_join_maaslins(bang_mal, c('_bang', '_mal'), mwi_variables_for_analysis, groups_to_analyse, 'metaphlan', maaslin_taxonomic_output_root_folder)

# View(combined_results$positive_coef)
# View(combined_results$negative_coef)

combined_results$positive_coef %>% filter(grepl('^s', lowest_taxonomic_level)) %>% 
  select(!c(metadata, value, N_bang, N.not.0_bang, pval_bang, N_mal, N.not.0_mal, pval_mal)) %>% 
  rename(Species = lowest_taxonomic_level, `Coefficient Bangladesh` = coef_bang, `Standard Error Bangladesh` = stderr_bang, `Q-value Bangladesh` = qval_bang, `Coefficient Malawi` = coef_mal, `Standard Error Malawi` = stderr_mal, `Q-value Malawi` = qval_mal) %>% 
  dplyr::mutate_if(is.numeric, funs(as.character(signif(., 3)))) %>% 
  kbl() %>% kable_styling()
feature Species Coefficient Bangladesh Standard Error Bangladesh Q-value Bangladesh Coefficient Malawi Standard Error Malawi Q-value Malawi
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Clostridiaceae.g__Clostridium.s__Clostridium_SGB6179 s__Clostridium_SGB6179 8.79 1.34 4.25e-05 5.52 1.6 0.0491
k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella.s__Prevotella_copri_clade_A s__Prevotella_copri_clade_A 4.54 0.978 0.00971 10.1 1.98 0.000989
k__Bacteria.p__Firmicutes.c__Negativicutes.o__Veillonellales.f__Veillonellaceae.g__GGB4266.s__GGB4266_SGB5809 s__GGB4266_SGB5809 4.58 1.09 0.0147 8.73 1.29 7.64e-06
k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Pasteurellales.f__Pasteurellaceae.g__Haemophilus.s__Haemophilus_parainfluenzae s__Haemophilus_parainfluenzae 3.64 0.979 0.03 6.85 1.11 5e-05
combined_results$negative_coef %>% filter(grepl('^s', lowest_taxonomic_level)) %>% 
  select(!c(metadata, value, N_bang, N.not.0_bang, pval_bang, N_mal, N.not.0_mal, pval_mal)) %>% 
  rename(Species = lowest_taxonomic_level, `Coefficient Bangladesh` = coef_bang, `Standard Error Bangladesh` = stderr_bang, `Q-value Bangladesh` = qval_bang, `Coefficient Malawi` = coef_mal, `Standard Error Malawi` = stderr_mal, `Q-value Malawi` = qval_mal) %>% 
  dplyr::mutate_if(is.numeric, funs(as.character(signif(., 3)))) %>% 
  kbl() %>% kable_styling()
feature Species Coefficient Bangladesh Standard Error Bangladesh Q-value Bangladesh Coefficient Malawi Standard Error Malawi Q-value Malawi
# todo - refactoring the run_combine_maaslins means that we dont get the species that are only associated at one site. need to fix that.

# nrow(combined_results$mwi_maaslin_only)
# nrow(combined_results$bang_maaslin_only)

mal_bang_maaslins <- rbind(combined_results$positive_coef, combined_results$negative_coef)
# View(combined_results$positive_coef )

# do an anti-join to get the species that are only associated at one site
bang_only <- anti_join(bangladesh_taxonomic_maaslin_filtered, mal_bang_maaslins, by = c('feature', 'metadata', 'value')) %>% filter(qval < 0.05)
mal_only <- anti_join(malawi_taxonomic_maaslin_filtered, mal_bang_maaslins, by = c('feature', 'metadata', 'value')) %>% filter(qval < 0.05)

bang_only %>% 
  rename(Species = lowest_taxonomic_level, `Coefficient` = coef, `Standard Error` = stderr, `Q-value` = qval) %>% 
  dplyr::mutate_if(is.numeric, funs(as.character(signif(., 3)))) %>% 
  kbl() %>% kable_styling()
feature Species metadata value Coefficient Standard Error N N.not.0 pval Q-value
k__Bacteria.p__Actinobacteria.c__Actinomycetia.o__Actinomycetales.f__Actinomycetaceae.g__Actinomyces.s__Actinomyces_naeslundii s__Actinomyces_naeslundii Group Control_HealthySerosurvey -3.27 0.543 80 48 5.7e-08 0.000216
k__Bacteria.p__Actinobacteria.c__Actinomycetia.o__Micrococcales.f__Cellulomonadaceae.g__Cellulomonas.s__Cellulomonas_flavigena s__Cellulomonas_flavigena Group Control_HealthySerosurvey -1.59 0.307 80 11 1.69e-06 0.00256
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Blautia.s__Blautia_glucerasea s__Blautia_glucerasea Group Control_HealthySerosurvey -4.97 0.987 80 41 3.23e-06 0.00407
k__Bacteria.p__Actinobacteria.c__Actinomycetia.o__Actinomycetales.f__Actinomycetaceae.g__Actinomyces.s__Actinomyces_oris s__Actinomyces_oris Group Control_HealthySerosurvey -3.73 0.749 80 51 3.99e-06 0.00431
k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella.s__Prevotella_copri_clade_C s__Prevotella_copri_clade_C Group Control_HealthySerosurvey 4.05 0.846 80 65 8.35e-06 0.0079
k__Bacteria.p__Actinobacteria.c__Actinomycetia.o__Actinomycetales.f__Actinomycetaceae.g__Actinomyces.s__Actinomyces_dentalis s__Actinomyces_dentalis Group Control_HealthySerosurvey -3.42 0.728 80 37 1.15e-05 0.00968
k__Bacteria.p__Actinobacteria.c__Actinomycetia.o__Actinomycetales.f__Actinomycetaceae.g__Actinomyces.s__Actinomyces_graevenitzii s__Actinomyces_graevenitzii Group Control_HealthySerosurvey -4.99 1.07 80 37 1.4e-05 0.00971
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Eubacteriaceae.g__Eubacterium.s__Eubacterium_ramulus s__Eubacterium_ramulus Group Control_HealthySerosurvey -3.27 0.817 80 26 0.000144 0.0147
k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Lactobacillaceae.g__Ligilactobacillus.s__Ligilactobacillus_ruminis s__Ligilactobacillus_ruminis Group Control_HealthySerosurvey 5.38 1.26 80 70 5.32e-05 0.0147
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Mediterraneibacter.s__Mediterraneibacter_butyricigenes s__Mediterraneibacter_butyricigenes Group Control_HealthySerosurvey -2.9 0.691 80 27 7.31e-05 0.0147
k__Bacteria.p__Actinobacteria.c__Actinomycetia.o__Actinomycetales.f__Actinomycetaceae.g__Pauljensenia.s__Pauljensenia_hongkongensis s__Pauljensenia_hongkongensis Group Control_HealthySerosurvey -3.15 0.716 80 27 3.51e-05 0.0147
k__Bacteria.p__Actinobacteria.c__Actinomycetia.o__Actinomycetales.f__Actinomycetaceae.g__Actinomyces.s__Actinomyces_timonensis s__Actinomyces_timonensis Group Control_HealthySerosurvey -0.522 0.133 80 3 0.000188 0.0182
k__Bacteria.p__Actinobacteria.c__Actinomycetia.o__Actinomycetales.f__Actinomycetaceae.g__Actinomyces.s__Actinomyces_sp_oral_taxon_448 s__Actinomyces_sp_oral_taxon_448 Group Control_HealthySerosurvey -2.71 0.728 80 23 0.000385 0.03
k__Bacteria.p__Actinobacteria.c__Actinomycetia.o__Actinomycetales.f__Actinomycetaceae.g__Actinomyces.s__Actinomyces_massiliensis s__Actinomyces_massiliensis Group Control_HealthySerosurvey -3.14 0.854 80 32 0.000447 0.0338
k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus.s__Streptococcus_SGB3665 s__Streptococcus_SGB3665 Group Control_HealthySerosurvey -1.39 0.38 80 6 0.000458 0.0343
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Oscillospiraceae.g__GGB9627.s__GGB9627_SGB15081 s__GGB9627_SGB15081 Group Control_HealthySerosurvey -2.43 0.666 80 22 0.000486 0.0357
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Lachnospiraceae_unclassified.s__Lachnospiraceae_bacterium s__Lachnospiraceae_bacterium Group Control_HealthySerosurvey -3.05 0.838 80 68 0.000502 0.0366
k__Bacteria.p__Firmicutes.c__Tissierellia.o__Tissierellales.f__Peptoniphilaceae.g__Peptoniphilus.s__Peptoniphilus_lacrimalis s__Peptoniphilus_lacrimalis Group Control_HealthySerosurvey -1.47 0.406 80 10 0.000536 0.0383
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Peptostreptococcaceae.g__Romboutsia.s__Romboutsia_timonensis s__Romboutsia_timonensis Group Control_HealthySerosurvey 4.52 1.25 80 66 0.000545 0.0386
mal_only %>% 
  rename(Species = lowest_taxonomic_level, `Coefficient` = coef, `Standard Error` = stderr, `Q-value` = qval) %>% 
  dplyr::mutate_if(is.numeric, funs(as.character(signif(., 3)))) %>% 
  kbl() %>% kable_styling()
feature Species metadata value Coefficient Standard Error N N.not.0 pval Q-value
k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Phocaeicola.s__Phocaeicola_massiliensis s__Phocaeicola_massiliensis Group Control_HealthySerosurvey 7.41 0.389 63 42 2.21e-25 3.93e-21
k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Sutterellaceae.g__GGB6565.s__GGB6565_SGB9274 s__GGB6565_SGB9274 Group Control_HealthySerosurvey 7.28 0.433 63 43 6.81e-23 4.85e-19
k__Bacteria.p__Proteobacteria.c__Deltaproteobacteria.o__Desulfovibrionales.f__Desulfovibrionaceae.g__Desulfovibrio.s__Desulfovibrio_SGB5077 s__Desulfovibrio_SGB5077 Group Control_HealthySerosurvey 8.33 0.614 63 44 7.07e-19 3.59e-15
k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Sutterellaceae.g__Duodenibacillus.s__Duodenibacillus_massiliensis s__Duodenibacillus_massiliensis Group Control_HealthySerosurvey 9.52 0.754 63 43 1.33e-17 4.73e-14
k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Phocaeicola.s__Phocaeicola_plebeius s__Phocaeicola_plebeius Group Control_HealthySerosurvey 8.13 0.705 63 42 4.59e-16 1.36e-12
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Lacrimispora.s__Lacrimispora_amygdalina s__Lacrimispora_amygdalina Group Control_HealthySerosurvey 5.89 0.548 63 45 6.16e-15 1.46e-11
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Coprococcus.s__Coprococcus_SGB4580 s__Coprococcus_SGB4580 Group Control_HealthySerosurvey 4.56 0.468 63 47 2.03e-13 4.02e-10
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Lachnospira.s__Lachnospira_sp_NSJ_43 s__Lachnospira_sp_NSJ_43 Group Control_HealthySerosurvey 4.07 0.441 63 41 1.36e-12 2.42e-09
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Anaerostipes.s__Anaerostipes_SGB4708 s__Anaerostipes_SGB4708 Group Control_HealthySerosurvey 8.32 0.93 63 46 3.67e-12 5.93e-09
k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Phocaeicola.s__Phocaeicola_coprocola s__Phocaeicola_coprocola Group Control_HealthySerosurvey 7.75 0.922 63 41 2.61e-11 3.87e-08
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Clostridiaceae.g__Clostridium.s__Clostridium_sp_AM33_3 s__Clostridium_sp_AM33_3 Group Control_HealthySerosurvey 8.75 1.08 63 47 7.08e-11 9.69e-08
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Oscillospiraceae.g__GGB9614.s__GGB9614_SGB15049 s__GGB9614_SGB15049 Group Control_HealthySerosurvey 3.9 0.497 63 43 2.01e-10 2.46e-07
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Eubacteriales_unclassified.g__Eubacteriales_unclassified.s__Eubacteriales_unclassified_SGB15145 s__Eubacteriales_unclassified_SGB15145 Group Control_HealthySerosurvey 6.23 0.802 63 41 2.64e-10 3.04e-07
k__Bacteria.p__Firmicutes.c__Erysipelotrichia.o__Erysipelotrichales.f__Erysipelotrichaceae.g__Faecalibacillus.s__Faecalibacillus_intestinalis s__Faecalibacillus_intestinalis Group Control_HealthySerosurvey 10.5 1.38 63 53 3.81e-10 4.11e-07
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Clostridiaceae.g__Clostridiaceae_unclassified.s__Clostridiaceae_bacterium_Marseille_Q4145 s__Clostridiaceae_bacterium_Marseille_Q4145 Group Control_HealthySerosurvey 5.57 0.737 63 45 5.74e-10 5.84e-07
k__Bacteria.p__Firmicutes.c__CFGB15212.o__OFGB15212.f__FGB15212.g__GGB41458.s__GGB41458_SGB58520 s__GGB41458_SGB58520 Group Control_HealthySerosurvey 4.74 0.66 63 44 2.27e-09 1.97e-06
k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Sutterellaceae.g__Sutterella.s__Sutterella_wadsworthensis s__Sutterella_wadsworthensis Group Control_HealthySerosurvey 8.97 1.28 63 46 4.6e-09 3.9e-06
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Clostridiaceae.g__Clostridiaceae_unclassified.s__Clostridiaceae_bacterium_Marseille_Q4143 s__Clostridiaceae_bacterium_Marseille_Q4143 Group Control_HealthySerosurvey 6.68 0.979 63 47 8.71e-09 7.05e-06
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Lachnospiraceae_unclassified.s__Lachnospiraceae_bacterium_AM48_27BH s__Lachnospiraceae_bacterium_AM48_27BH Group Control_HealthySerosurvey 4.33 0.64 63 44 1.05e-08 7.64e-06
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Lachnospira.s__Lachnospira_pectinoschiza s__Lachnospira_pectinoschiza Group Control_HealthySerosurvey 7.18 1.06 63 42 1.14e-08 7.95e-06
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Clostridiaceae.g__Clostridiaceae_unclassified.s__Clostridiaceae_bacterium_AF18_31LB s__Clostridiaceae_bacterium_AF18_31LB Group Control_HealthySerosurvey 4.69 0.706 63 49 1.72e-08 1.15e-05
k__Bacteria.p__Firmicutes.c__Firmicutes_unclassified.o__Firmicutes_unclassified.f__Firmicutes_unclassified.g__GGB9511.s__GGB9511_SGB14908 s__GGB9511_SGB14908 Group Control_HealthySerosurvey 6.13 0.934 63 45 2.32e-08 1.47e-05
k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides.s__Bacteroides_eggerthii s__Bacteroides_eggerthii Group Control_HealthySerosurvey 4.54 0.723 63 41 6.55e-08 3.76e-05
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Mediterraneibacter.s__Ruminococcus_lactaris s__Ruminococcus_lactaris Group Control_HealthySerosurvey 8.9 1.43 63 49 8.14e-08 4.2e-05
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Clostridiaceae.g__Clostridium.s__Clostridium_sp_AM22_11AC s__Clostridium_sp_AM22_11AC Group Control_HealthySerosurvey 8.74 1.4 63 52 7.6e-08 4.2e-05
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Roseburia.s__Roseburia_intestinalis s__Roseburia_intestinalis Group Control_HealthySerosurvey 8.59 1.38 63 45 8.1e-08 4.2e-05
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Blautia.s__Blautia_stercoris s__Blautia_stercoris Group Control_HealthySerosurvey 9.71 1.58 63 50 1.01e-07 4.92e-05
k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides.s__Bacteroides_caccae s__Bacteroides_caccae Group Control_HealthySerosurvey 5.55 0.936 63 46 2.37e-07 9.6e-05
k__Bacteria.p__Firmicutes.c__CFGB1422.o__OFGB1422.f__FGB1422.g__GGB3486.s__GGB3486_SGB4658 s__GGB3486_SGB4658 Group Control_HealthySerosurvey 5.69 0.96 63 41 2.37e-07 9.6e-05
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Clostridiaceae.g__Clostridium.s__Clostridium_fessum s__Clostridium_fessum Group Control_HealthySerosurvey 4.55 0.779 63 50 3.15e-07 0.000119
k__Bacteria.p__Proteobacteria.c__Deltaproteobacteria.o__Desulfovibrionales.f__Desulfovibrionaceae.g__Bilophila.s__Bilophila_SGB15450 s__Bilophila_SGB15450 Group Control_HealthySerosurvey 5.3 0.929 63 40 5.22e-07 0.00019
k__Bacteria.p__Bacteroidetes.c__CFGB629.o__OFGB629.f__FGB629.g__GGB1495.s__GGB1495_SGB2071 s__GGB1495_SGB2071 Group Control_HealthySerosurvey 6.84 1.21 63 50 6.4e-07 0.000215
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Clostridiaceae.g__Clostridium.s__Clostridium_sp_AF12_28 s__Clostridium_sp_AF12_28 Group Control_HealthySerosurvey 3.95 0.71 63 45 8.78e-07 0.000281
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Roseburia.s__Roseburia_faecis s__Roseburia_faecis Group Control_HealthySerosurvey 7.65 1.38 63 49 9.66e-07 0.000304
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Eubacteriaceae.g__Eubacterium.s__Eubacterium_sp_OM08_24 s__Eubacterium_sp_OM08_24 Group Control_HealthySerosurvey 6.1 1.11 63 47 1.24e-06 0.000383
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Oscillospiraceae.g__Agathobaculum.s__Agathobaculum_butyriciproducens s__Agathobaculum_butyriciproducens Group Control_HealthySerosurvey 4.45 0.825 63 55 1.65e-06 0.00049
k__Bacteria.p__Firmicutes.c__Negativicutes.o__Veillonellales.f__Veillonellaceae.g__Veillonella.s__Veillonella_dispar s__Veillonella_dispar Group Control_HealthySerosurvey 4.7 0.883 63 45 2.12e-06 0.000609
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Clostridiaceae.g__Clostridium.s__Clostridium_perfringens s__Clostridium_perfringens Group Control_HealthySerosurvey 6.21 1.18 63 47 2.46e-06 0.00068
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Faecalicatena.s__Faecalicatena_fissicatena s__Faecalicatena_fissicatena Group Control_HealthySerosurvey 8.04 1.55 63 52 3.52e-06 0.000936
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Roseburia.s__Roseburia_inulinivorans s__Roseburia_inulinivorans Group Control_HealthySerosurvey 6.24 1.22 63 58 4.17e-06 0.000989
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Oscillospiraceae.g__GGB9616.s__GGB9616_SGB15052 s__GGB9616_SGB15052 Group Control_HealthySerosurvey 3.83 0.749 63 47 4.4e-06 0.000989
k__Bacteria.p__Firmicutes.c__Negativicutes.o__Veillonellales.f__Veillonellaceae.g__Veillonella.s__Veillonella_atypica s__Veillonella_atypica Group Control_HealthySerosurvey 6.27 1.22 63 44 4.23e-06 0.000989
k__Bacteria.p__Firmicutes.c__CFGB76639.o__OFGB76639.f__FGB76639.g__GGB2658.s__GGB2658_SGB3579 s__GGB2658_SGB3579 Group Control_HealthySerosurvey -0.321 0.0628 63 1 4.5e-06 0.000989
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__GGB3619.s__GGB3619_SGB4895 s__GGB3619_SGB4895 Group Control_HealthySerosurvey -0.321 0.0628 63 1 4.5e-06 0.000989
k__Bacteria.p__Bacteroidetes.c__CFGB76185.o__OFGB76185.f__FGB76185.g__GGB1550.s__GGB1550_SGB2134 s__GGB1550_SGB2134 Group Control_HealthySerosurvey -2.53 0.492 63 3 3.98e-06 0.000989
k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Eggerthellales.f__Eggerthellaceae.g__Paraeggerthella.s__Paraeggerthella_hongkongensis s__Paraeggerthella_hongkongensis Group Control_HealthySerosurvey -0.321 0.0628 63 1 4.5e-06 0.000989
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Christensenellaceae.g__Christensenella.s__Christensenella_minuta s__Christensenella_minuta Group Control_HealthySerosurvey -0.321 0.0628 63 1 4.5e-06 0.000989
k__Bacteria.p__Firmicutes.c__Firmicutes_unclassified.o__Firmicutes_unclassified.f__Firmicutes_unclassified.g__Firmicutes_unclassified.s__Firmicutes_bacterium_AF16_15 s__Firmicutes_bacterium_AF16_15 Group Control_HealthySerosurvey 5.44 1.08 63 50 5.57e-06 0.00119
k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Eggerthellales.f__Eggerthellaceae.g__GGB9420.s__GGB9420_SGB14786 s__GGB9420_SGB14786 Group Control_HealthySerosurvey -1.28 0.258 63 2 7.42e-06 0.00144
k__Bacteria.p__Firmicutes.c__Negativicutes.o__Veillonellales.f__Veillonellaceae.g__Veillonella.s__Veillonella_tobetsuensis s__Veillonella_tobetsuensis Group Control_HealthySerosurvey 5.01 1.02 63 41 9.5e-06 0.00174
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Coprococcus.s__Coprococcus_SGB4669 s__Coprococcus_SGB4669 Group Control_HealthySerosurvey 7.1 1.46 63 54 1.02e-05 0.00185
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Clostridiaceae.g__Clostridium.s__Clostridium_sp_AF27_2AA s__Clostridium_sp_AF27_2AA Group Control_HealthySerosurvey 6.05 1.25 63 44 1.09e-05 0.00196
k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Prevotellaceae.g__Prevotella.s__Prevotella_SGB1680 s__Prevotella_SGB1680 Group Control_HealthySerosurvey -1.58 0.33 63 3 1.33e-05 0.00229
k__Bacteria.p__Firmicutes.c__CFGB72899.o__OFGB72899.f__FGB72899.g__GGB4608.s__GGB4608_SGB6382 s__GGB4608_SGB6382 Group Control_HealthySerosurvey -4.77 0.997 63 3 1.4e-05 0.00238
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Clostridiaceae.g__Clostridium.s__Clostridium_paraputrificum s__Clostridium_paraputrificum Group Control_HealthySerosurvey 6.51 1.36 63 42 1.44e-05 0.00243
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Enterocloster.s__Enterocloster_bolteae s__Enterocloster_bolteae Group Control_HealthySerosurvey -2.19 0.459 63 5 1.49e-05 0.00247
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Clostridiaceae.g__Clostridium.s__Clostridium_saudiense s__Clostridium_saudiense Group Control_HealthySerosurvey 4.96 1.1 63 48 3.56e-05 0.00499
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Blautia.s__Blautia_obeum s__Blautia_obeum Group Control_HealthySerosurvey 4.32 0.974 63 60 4.63e-05 0.0062
k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus.s__Streptococcus_salivarius s__Streptococcus_salivarius Group Control_HealthySerosurvey 8.23 1.86 63 55 4.68e-05 0.00622
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Oscillospiraceae.g__Ruminococcus.s__Ruminococcus_bromii s__Ruminococcus_bromii Group Control_HealthySerosurvey -7.54 1.72 63 18 5.56e-05 0.00712
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Mediterraneibacter.s__Ruminococcus_gnavus s__Ruminococcus_gnavus Group Control_HealthySerosurvey 7.4 1.72 63 46 7.39e-05 0.00903
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Clostridiaceae.g__Clostridium.s__Clostridium_celatum s__Clostridium_celatum Group Control_HealthySerosurvey 4.8 1.13 63 41 8.23e-05 0.0099
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Oscillospiraceae.g__Faecalibacterium.s__Faecalibacterium_prausnitzii s__Faecalibacterium_prausnitzii Group Control_HealthySerosurvey 5.53 1.3 63 60 8.7e-05 0.0102
k__Bacteria.p__Firmicutes.c__CFGB1311.o__OFGB1311.f__FGB1311.g__GGB3141.s__GGB3141_SGB4154 s__GGB3141_SGB4154 Group Control_HealthySerosurvey -4.04 0.951 63 6 8.76e-05 0.0102
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Oscillospiraceae.g__Faecalibacterium.s__Faecalibacterium_SGB15346 s__Faecalibacterium_SGB15346 Group Control_HealthySerosurvey 5.98 1.42 63 58 0.000104 0.0119
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Dorea.s__Dorea_formicigenerans s__Dorea_formicigenerans Group Control_HealthySerosurvey 3.68 0.878 63 58 0.000106 0.0121
k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides.s__Bacteroides_salyersiae s__Bacteroides_salyersiae Group Control_HealthySerosurvey 3.05 0.728 63 42 0.000108 0.0122
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Clostridiaceae.g__Clostridium.s__Clostridium_sp_AF20_17LB s__Clostridium_sp_AF20_17LB Group Control_HealthySerosurvey 2.47 0.597 63 45 0.000128 0.0135
k__Bacteria.p__Firmicutes.c__Erysipelotrichia.o__Erysipelotrichales.f__Turicibacteraceae.g__Turicibacter.s__Turicibacter_sanguinis s__Turicibacter_sanguinis Group Control_HealthySerosurvey 4.77 1.17 63 44 0.000154 0.0154
k__Bacteria.p__Firmicutes.c__Erysipelotrichia.o__Erysipelotrichales.f__Coprobacillaceae.g__Coprobacillus.s__Coprobacillus_cateniformis s__Coprobacillus_cateniformis Group Control_HealthySerosurvey -1.61 0.401 63 3 0.00019 0.0179
k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Tannerellaceae.g__Parabacteroides.s__Parabacteroides_merdae s__Parabacteroides_merdae Group Control_HealthySerosurvey 5.44 1.38 63 42 0.000227 0.0204
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Oscillospiraceae.g__Oscillospiraceae_unclassified.s__Oscillospiraceae_unclassified_SGB15257 s__Oscillospiraceae_unclassified_SGB15257 Group Control_HealthySerosurvey 6.88 1.74 63 50 0.000234 0.0208
k__Bacteria.p__Actinobacteria.c__Coriobacteriia.o__Coriobacteriales.f__Coriobacteriaceae.g__Collinsella.s__Collinsella_aerofaciens s__Collinsella_aerofaciens Group Control_HealthySerosurvey 6.12 1.56 63 56 0.000256 0.022
k__Archaea.p__Thaumarchaeota.c__Thaumarchaeota_unclassified.o__Nitrosopumilales.f__Nitrosopumilaceae.g__Nitrosopumilus.s__Nitrosopumilus_SGB14899 s__Nitrosopumilus_SGB14899 Group Control_HealthySerosurvey -2.08 0.533 63 8 0.000271 0.0223
k__Bacteria.p__Firmicutes.c__CFGB1473.o__OFGB1473.f__FGB1473.g__GGB3730.s__GGB3730_SGB5060 s__GGB3730_SGB5060 Group Control_HealthySerosurvey -3.14 0.82 63 9 0.000337 0.0229
k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Lactococcus.s__Lactococcus_garvieae s__Lactococcus_garvieae Group Control_HealthySerosurvey -1.38 0.358 63 3 0.000316 0.0229
k__Bacteria.p__Actinobacteria.c__Actinomycetia.o__Corynebacteriales.f__Corynebacteriaceae.g__Corynebacterium.s__Corynebacterium_propinquum s__Corynebacterium_propinquum Group Control_HealthySerosurvey 0.879 0.228 63 2 0.000319 0.0229
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Mediterraneibacter.s__Ruminococcus_torques s__Ruminococcus_torques Group Control_HealthySerosurvey 4.38 1.15 63 60 0.000352 0.0231
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Lachnospiraceae.g__Lachnospiraceae_unclassified.s__Lachnospiraceae_bacterium_NSJ_46 s__Lachnospiraceae_bacterium_NSJ_46 Group Control_HealthySerosurvey 3.48 0.928 63 49 0.00043 0.0265
k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Bacteroidaceae.g__Bacteroides.s__Bacteroides_congonensis s__Bacteroides_congonensis Group Control_HealthySerosurvey 4.33 1.17 63 30 0.000501 0.0289
k__Bacteria.p__Actinobacteria.c__Actinomycetia.o__Actinomycetales.f__Actinomycetaceae.g__Actinomyces.s__Actinomyces_sp_HMSC035G02 s__Actinomyces_sp_HMSC035G02 Group Control_HealthySerosurvey -1.48 0.401 63 5 0.000542 0.0308
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Oscillospiraceae.g__GGB9737.s__GGB9737_SGB15309 s__GGB9737_SGB15309 Group Control_HealthySerosurvey 7.27 1.98 63 48 0.000554 0.0313
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Clostridiaceae.g__Butyricicoccus.s__Butyricicoccus_sp_AM29_23AC s__Butyricicoccus_sp_AM29_23AC Group Control_HealthySerosurvey 5.51 1.52 63 52 0.000658 0.0352
k__Bacteria.p__Firmicutes.c__CFGB3068.o__OFGB3068.f__FGB3068.g__GGB9760.s__GGB9760_SGB15372 s__GGB9760_SGB15372 Group Control_HealthySerosurvey 4.1 1.14 63 54 0.000691 0.0363
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Oscillospiraceae.g__Ruminococcus.s__Ruminococcus_sp_NSJ_13 s__Ruminococcus_sp_NSJ_13 Group Control_HealthySerosurvey 5.19 1.44 63 52 0.000725 0.0372
k__Bacteria.p__Bacteroidetes.c__Bacteroidia.o__Bacteroidales.f__Rikenellaceae.g__Alistipes.s__Alistipes_putredinis s__Alistipes_putredinis Group Control_HealthySerosurvey 4.42 1.25 63 51 0.000839 0.0415
k__Bacteria.p__Firmicutes.c__Clostridia.o__Eubacteriales.f__Eubacteriales_Family_XIII_Incertae_Sedis.g__GGB2977.s__GGB2977_SGB3959 s__GGB2977_SGB3959 Group Control_HealthySerosurvey -2.51 0.709 63 13 0.000849 0.0415
k__Bacteria.p__Firmicutes.c__CFGB15209.o__OFGB15209.f__FGB15209.g__GGB9293.s__GGB9293_SGB14250 s__GGB9293_SGB14250 Group Control_HealthySerosurvey -2.82 0.802 63 15 0.000921 0.0434

There were species significantly (q-val < 0.05) associated with health/disease in Malawi only and in Bangladesh only.

The ones associated at only one site are written out to a file, you can look at them manually there.

1.5.4 Forest plot

Setting up the forest plots. Need to combine all three maaslin outputs, and then add in the patch data.

# combine the maaslines for bgd and mal, and then add in the nepal ones.
combined_maaslins <- inner_join_maaslins(bangladesh_taxonomic_maaslin, malawi_taxonomic_maaslin, '_bang', '_mal', 'metaphlan')
combined_maaslins <- inner_join_maaslins(combined_maaslins, nepal_taxonomic_maaslin, 'not', 'used', 'metaphlan')
# for the nepal columns, give them a "_nep" suffix
combined_maaslins <- combined_maaslins %>%
  rename_with(~ paste0(.x, '_nep'), c("coef", "stderr", "N", "N.not.0", "pval", "qval"))

# get the patch data
patch_taxonomic_maaslin <- read_tsv(file.path(patch_maaslin_taxonomic_output_root_folder, 'baseline_typhi_species', 'all_results.tsv'))
patch_taxonomic_maaslin$lowest_taxonomic_level <- sapply(str_split(patch_taxonomic_maaslin$feature, "\\."), function(x) x[length(x)])
patch_taxonomic_maaslin <- patch_taxonomic_maaslin %>% relocate(lowest_taxonomic_level, .after = feature)
patch_taxonomic_maaslin <- patch_taxonomic_maaslin %>%
  rename_with(~ paste0(.x, '_patch'), c("coef", "stderr", "N", "N.not.0", "pval", "qval"))

# in patch_taxonomic_maaslin, change all Diagnosis in metadata column to Group, and all 'no_disease' in value column to 'ControlHealthySerosurvey'
# this is to match the other maaslin outputs
patch_taxonomic_maaslin <- patch_taxonomic_maaslin %>% filter(metadata == 'Diagnosis')
# in patch_taxonomic_maaslin, change all Diagnosis in metadata column to Group, and all 'no_disease' in value column to 'ControlHealthySerosurvey'
patch_taxonomic_maaslin$metadata <- ifelse(patch_taxonomic_maaslin$metadata == 'Diagnosis', 'Group', patch_taxonomic_maaslin$metadata)
patch_taxonomic_maaslin$value <- ifelse(patch_taxonomic_maaslin$value == 'no_disease', 'Control_HealthySerosurvey', patch_taxonomic_maaslin$value)

# join the patch data to the combined maaslin data
combined_maaslins <- combined_maaslins %>% left_join(patch_taxonomic_maaslin, by = c("feature", 'metadata', 'value', 'lowest_taxonomic_level'))

for the forest plot, i also want to include the per-cohort abundance medians for the species of interest.

groups_to_analyse <- c('Acute typhoid', 'Household contact')
prevalence <- get_prevalence(strataa_metaphlan_data_species, groups_to_analyse)

do the forest plot

# prevalence is the relative abundance data, the species of interest are the ones that are significantly associated with health/disease in two countries, and combined_maaslins is the maaslin output with the associations
run_forest_plot(prevalence, c('s__Prevotella_copri_clade_A', 's__Clostridium_SGB6179', 's__GGB4266_SGB5809', 's__Haemophilus_parainfluenzae'), combined_maaslins)

1.5.5 plots of species of interest

strataa_metaphlan_data_longer <- strataa_metaphlan_data %>% mutate(feature = rownames(strataa_metaphlan_data)) %>% pivot_longer(!c(feature, lowest_taxonomic_level), names_to = "SampleID", values_to = "prevalence")
# View(head(strataa_metaphlan_data_longer))
strataa_metaphlan_data_longer_meta <- strataa_metaphlan_data_longer %>% left_join(metadata, by = c("SampleID" = "SampleID"))

pc <- run_plot_species_of_interest(strataa_metaphlan_data_longer_meta, 's__Prevotella_copri_clade_A', participant_group_colours)

cs <- run_plot_species_of_interest(strataa_metaphlan_data_longer_meta, 's__Clostridium_SGB6179', participant_group_colours)

SGB5809 <-run_plot_species_of_interest(strataa_metaphlan_data_longer_meta, 's__GGB4266_SGB5809', participant_group_colours)

hp <- run_plot_species_of_interest(strataa_metaphlan_data_longer_meta, 's__Haemophilus_parainfluenzae', participant_group_colours)

# rt <- run_plot_species_of_interest(strataa_metaphlan_data_longer_meta, 's__Romboutsia_timonensis', participant_group_colours)
# lb <- run_plot_species_of_interest(strataa_metaphlan_data_longer_meta, 's__Lachnospiraceae_bacterium', participant_group_colours)

# pc
  # pc + cs + SGB5809 + hp + rt + lb

1.6 healthy vs carrier

1.6.1 phylum plots

# metadata_for_phyla_plots <- metadata %>% dplyr::select(SampleID, Group, Country)
phyla_clean_metadata <- prep_data_to_plot_phyla(strataa_metaphlan_data, metadata)
order_of_groups <- c("High Vi-titre", "Household contact")
# bangladesh_phyla_plot <- plot_per_country_abundance(phyla_clean_metadata = phyla_clean_metadata, country = "Bangladesh", group_order = order_of_groups)
# bangladesh_phyla_plot
malawi_phyla_plot <- plot_per_country_abundance(phyla_clean_metadata = phyla_clean_metadata, country = "Malawi", group_order = order_of_groups)
nepal_phyla_plot <- plot_per_country_abundance(phyla_clean_metadata = phyla_clean_metadata, country = "Nepal", group_order = order_of_groups)

# bangladesh_phyla_plot / 
malawi_phyla_plot / nepal_phyla_plot + plot_layout(guides = 'collect')

1.6.2 alpha diversity

Alpha diversity - all countries, healthy and carrier

all_countries_healthy_carrier_alpha <- metaphlan_alpha(strataa_metaphlan_data_species, metadata, countries_of_interest = c('Malawi', 'Nepal'), groups_of_interest = c('High Vi-titre', 'Household contact'), comparisons = list(c('High Vi-titre', 'Household contact')), participant_group_colours)

all_countries_healthy_carrier_alpha$alpha_by_country

all_countries_healthy_carrier_alpha$alpha_anova_summary_with_var_names %>% dplyr::mutate_if(is.numeric, funs(as.character(signif(., 3)))) %>% kbl() %>% kable_styling()
rownames(alpha_anova_summary[[1]]) Df Sum.Sq Mean.Sq F.value Pr..F. is_it_significant
Country:Group 1 9.7 9.7 32.3 1.12e-07 significant
Group 1 1.65 1.65 5.49 0.0209 not_significant
Group:Age 1 1.65 1.65 5.49 0.021 not_significant
Country 1 1.32 1.32 4.38 0.0387 not_significant
Country:Sex:Age 1 0.394 0.394 1.31 0.255 not_significant
Sex:Group 1 0.382 0.382 1.27 0.262 not_significant
Country:Group:Age 1 0.254 0.254 0.844 0.36 not_significant
Sex 1 0.0687 0.0687 0.229 0.634 not_significant
Age 1 0.0611 0.0611 0.203 0.653 not_significant
Country:Sex:Group:Age 1 0.0412 0.0412 0.137 0.712 not_significant
Country:Age 1 0.0393 0.0393 0.131 0.718 not_significant
Country:Sex:Group 1 0.0326 0.0326 0.108 0.743 not_significant
Sex:Group:Age 1 0.0323 0.0323 0.107 0.744 not_significant
Country:Sex 1 0.00583 0.00583 0.0194 0.89 not_significant
Sex:Age 1 0.000729 0.000729 0.00242 0.961 not_significant
Residuals 110 33.1 0.301 NA NA NA
all_countries_healthy_carrier_alpha$alpha_plot_group

1.6.3 beta diversity

High Vi-titre vs healthy.

all_countries_beta_carrier_healthy <- strataa_metaphlan_beta(strataa_metaphlan_data_species, metadata, c('Malawi', 'Nepal'), c('High Vi-titre', 'Household contact'), participant_group_colours)

all_countries_beta_carrier_healthy$pn_res %>% kbl %>% kable_styling()
R2 Pr(>F) is_it_significant
Group 0.0861708 0.0009990 significant
Group:Age 0.0142600 0.0199800 not_significant
Age 0.0115688 0.0639361 not_significant
Sex:Age 0.0111861 0.0839161 not_significant
Sex 0.0082620 0.2517483 not_significant
Sex:Group:Age 0.0078819 0.3546454 not_significant
Sex:Group 0.0070955 0.4265734 not_significant
Total 1.0000000 NA NA
Residual 0.8535749 NA NA
# bgd_beta_carrier_healthy <- strataa_metaphlan_beta(strataa_metaphlan_data_species, metadata, c('Bangladesh'), c('High Vi-titre', 'Household contact'))
# bgd_beta_carrier_healthy$pn_res %>% kbl %>% kable_styling()

mal_beta_carrier_healthy <- strataa_metaphlan_beta(strataa_metaphlan_data_species, metadata, c('Malawi'), c('High Vi-titre', 'Household contact'), participant_group_colours)

mal_beta_carrier_healthy$pn_res %>% kbl %>% kable_styling()
R2 Pr(>F) is_it_significant
Group 0.2418944 0.0009990 significant
Sex:Age 0.0229140 0.0079920 significant
Age 0.0164550 0.0819181 not_significant
Group:Age 0.0127858 0.1598402 not_significant
Sex 0.0131844 0.1638362 not_significant
Sex:Group 0.0105540 0.3036963 not_significant
Sex:Group:Age 0.0051988 0.8701299 not_significant
Total 1.0000000 NA NA
Residual 0.6770134 NA NA
nep_beta_carrier_healthy <- strataa_metaphlan_beta(strataa_metaphlan_data_species, metadata, c('Nepal'), c('High Vi-titre', 'Household contact'), participant_group_colours)

nep_beta_carrier_healthy$pn_res %>% kbl %>% kable_styling()
R2 Pr(>F) is_it_significant
Group 0.0499326 0.0009990 significant
Sex:Group 0.0246972 0.2397602 not_significant
Sex 0.0219270 0.4055944 not_significant
Age 0.0212371 0.4525475 not_significant
Sex:Age 0.0206648 0.4685315 not_significant
Sex:Group:Age 0.0178023 0.7562438 not_significant
Group:Age 0.0168350 0.8241758 not_significant
Total 1.0000000 NA NA
Residual 0.8269040 NA NA
mal_beta_carrier_healthy$pc12 + nep_beta_carrier_healthy$pc12 + plot_layout(guides = 'collect')

1.6.4 maaslin taxonomic groups

# groups_to_analyse <- c('Acute_typhi', 'Control_HealthySerosurvey')

groups_to_analyse <- c('Carrier', 'Control_HealthySerosurvey')
# bang_variables_for_analysis <- c("Group", "Sex", "Age")
mwi_variables_for_analysis <- c("Group", "Sex", "Age", "sequencing_lane")
nep_variables_for_analysis <- c("Group", "Sex", "Age")
# bangladesh_taxonomic_maaslin <- read_in_maaslin('Bangladesh', groups_to_analyse, bang_variables_for_analysis, 'metaphlan')
malawi_taxonomic_maaslin <- read_in_maaslin('Malawi', groups_to_analyse, mwi_variables_for_analysis, 'metaphlan')
nepal_taxonomic_maaslin <- read_in_maaslin('Nepal', groups_to_analyse, nep_variables_for_analysis, 'metaphlan')
# bangladesh_taxonomic_maaslin_filtered <- filter_taxonomic_maaslin(bangladesh_taxonomic_maaslin)
malawi_taxonomic_maaslin_filtered <- filter_taxonomic_maaslin(malawi_taxonomic_maaslin)
nepal_taxonomic_maaslin_filtered <- filter_taxonomic_maaslin(nepal_taxonomic_maaslin)
# bangladesh_maaslin_stats <- basic_maaslin_stats(bangladesh_taxonomic_maaslin_filtered, 'Bangladesh', bang_variables_for_analysis, groups_to_analyse)
malawi_maaslin_stats <- basic_maaslin_stats(malawi_taxonomic_maaslin_filtered, 'Malawi', mwi_variables_for_analysis, groups_to_analyse)
nepal_maaslin_stats <- basic_maaslin_stats(nepal_taxonomic_maaslin_filtered, 'Nepal', nep_variables_for_analysis, groups_to_analyse)
nrow(malawi_maaslin_stats$maaslin_results_sig)
## [1] 125
nrow(nepal_maaslin_stats$maaslin_results_sig)
## [1] 1

We’re not including the bangladesh samples in this analysis, because the bangladesh carriers were processed differently (extracted without being frozen).

There are no species significantly associated with carrier status in both Mal and Nep, so not doing the combine.